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000917553 0247_ $$2doi$$a10.48550/ARXIV.2208.04852
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000917553 037__ $$aFZJ-2023-00755
000917553 1001_ $$0P:(DE-HGF)0$$aRittig, Jan G.$$b0
000917553 245__ $$aGraph neural networks for the prediction of molecular structure-property relationships
000917553 260__ $$barXiv$$c2022
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000917553 520__ $$aMolecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear model. In contrast, graph neural networks, a novel machine learning method, directly work on the molecular graph, i.e., a graph representation where atoms correspond to nodes and bonds correspond to edges. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors as in QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction performance on various property predictions tasks and represent an active field of research. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
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000917553 650_7 $$2Other$$aBiomolecules (q-bio.BM)
000917553 650_7 $$2Other$$aMachine Learning (cs.LG)
000917553 650_7 $$2Other$$aOptimization and Control (math.OC)
000917553 650_7 $$2Other$$aFOS: Biological sciences
000917553 650_7 $$2Other$$aFOS: Computer and information sciences
000917553 650_7 $$2Other$$aFOS: Mathematics
000917553 7001_ $$0P:(DE-HGF)0$$aGao, Qinghe$$b1
000917553 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b2$$ufzj
000917553 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b3$$ufzj
000917553 7001_ $$0P:(DE-HGF)0$$aSchweidtmann, Artur M.$$b4$$eCorresponding author
000917553 773__ $$a10.48550/ARXIV.2208.04852
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